Computer Vision Algorithms, Vehicle Navigation and Remote Sensing Technologies, and Smart Traffic Planning and Analytics in Urban Transportation Systems
Computer Vision Algorithms, Vehicle Navigation and Remote Sensing Technologies, and Smart Traffic Planning and Analytics in Urban Transportation Systems

Summary/Abstract: I draw on a substantial body of theoretical and empirical research on computer vision algorithms, vehicle navigation and remote sensing technologies, and smart traffic planning and analytics in urban transportation systems. With increasing evidence of motion planning and data visualization tools, autonomous vehicle perception sensors, and crash avoidance technologies, there is an essential demand for comprehending whether intelligent traffic monitoring systems require simulation and virtualization technologies, connected car data, and autonomous vehicle planning algorithms. In this research, prior findings were cumulated indicating that sensing and computing technologies integrate location tracking algorithms, simulation modeling and mobility data processing tools, and monitoring and predictive capabilities. I carried out a quantitative literature review of ProQuest, Scopus, and the Web of Science throughout March 2022, with search terms including “urban transportation systems” + “computer vision algorithms,” “vehicle navigation and remote sensing technologies,” and “smart traffic planning and analytics.” As I analyzed research published in 2021 and 2022, only 92 papers met the eligibility criteria. By removing controversial or unclear findings (scanty/unimportant data), results unsupported by replication, undetailed content, or papers having quite similar titles, I decided on 15, chiefly empirical, sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AMSTAR, Distiller SR, ROBIS, and SRDR.

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